Break design/ files into subfolders by ** heading (43 new files)

projects/passepartout/architecture/design/implementation-properties/_index.org

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+
+:PROPERTIES:
+:CREATED:  [2026-05-11 Mon]
+:ID:       617a1031-495d-438c-a8e8-6e066b364e41
+:END:
+* Implementation Properties
+
+- [[file:performance-why-ontology-growth-doesnt-make-the-system-slower.org][Performance — Why Ontology Growth Doesn't Make the System Slower]] — Passepartout's performance thesis is: minimize LLM calls, minimize context token
+- [[file:the-provenance-chain-as-product.org][The Provenance Chain as Product]] — In the coding domain, the value of the symbolic engine is the verified fact: "th

projects/passepartout/architecture/design/implementation-properties/performance-why-ontology-growth-doesnt-make-the-system-slower.org

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+---
+title: Performance — Why Ontology Growth Doesn't Make the System Slower
+type: reference
+tags: :passepartout:architecture:
+---
+
+* Performance — Why Ontology Growth Doesn't Make the System Slower
+:PROPERTIES:
+:ID:       772ae489-b10a-48a0-bc3b-29136163d45b
+:ID:       design-performance
+:CREATED:  [2026-05-10 Sun]
+:WEIGHT:  40
+:END:
+
+Passepartout's performance thesis is: minimize LLM calls, minimize context tokens, keep everything else local and fast. Knowledge base size is irrelevant to those metrics. This is not an aspiration. It is a structural property.
+
+The system has two cost domains with fundamentally different scaling:
+
+| Resource      | Cost driver                              | Scales with                             |
+|---------------+------------------------------------------+------------------------------------------|
+| LLM tokens    | Context window size, number of API calls | Foveal-peripheral pruning, gate rules    |
+| Compute       | Screamer deduction, hash table lookups   | Entity count, rule count per domain      |
+
+LLM tokens are minimized by design — deterministic gates cost 0 tokens, sparse-tree rendering keeps context at 2,000–4,000 tokens regardless of memex size. Adding 5 million Wikidata entities doesn't add a single token to any LLM call. The education is local. Only the brain costs.
+
+Compute grows linearly with entity count (hash table lookups are O(1), but memory footprint grows). It grows with rule count within a single domain during Screamer consistency checking. But these are microsecond costs on local hardware, not API bills. A Screamer constraint check against a domain with 200 rules costs ~0.3ms. A 100-token guardrail paragraph in a system prompt costs ~$0.00001. The Screamer check is 10,000x cheaper and convergent — it handles the rule once. The guardrail paragraph handles it on every call, forever.
+
+A 5-million-entity Wikidata load is ~400MB in a hash table. A lifetime personal memex with a decade of diary entries is perhaps 10-20 million triples (~1.5GB). Modern laptops carry 16-64GB. The knowledge base fits in consumer hardware with room for the Lisp runtime, the memory-object store, and the LLM inference engine.
+
+*One genuine risk — rule generalization width.* If Screamer deduces increasingly broad rules within a single domain, the constraint space could bloat. Mitigation: rules carry a =:domain= tag. Screamer only applies rules from the fact's domain. Rule generalization that crosses domain boundaries is gated — must be human-approved. Rules that prove unused (never triggered a check in N heartbeat cycles) are demoted to =:inactive= and excluded from the active constraint set.
+
+This is the minimalism argument restated in concrete terms: you buy bigger RAM and a faster CPU once. You don't buy bigger LLM context windows on every call. The education is a capital investment. The brain is an operating expense. The architecture makes the ratio favor capital.

projects/passepartout/architecture/design/implementation-properties/the-provenance-chain-as-product.org

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+---
+title: The Provenance Chain as Product
+type: reference
+tags: :passepartout:architecture:
+---
+
+* The Provenance Chain as Product
+:PROPERTIES:
+:ID:       2e3e576e-8839-4566-bae1-f40137428bb9
+:ID:       design-provenance-product
+:CREATED:  [2026-05-10 Sun]
+:WEIGHT:  40
+:END:
+
+In the coding domain, the value of the symbolic engine is the verified fact: "this command is safe." In the broader memex, the value is the provenance itself: "this claim originated in that diary entry on that date, has been referenced 7 times across 4 different projects, was contradicted in a retrospective 6 months later, and was revised in a note 3 weeks after that."
+
+The symbolic engine doesn't tell you what is true. It tells you what you wrote, when, where, and how it connects to everything else you wrote — with a verifiable audit trail. It is a memory prosthesis that makes your own mind legible to you.
+
+Every fact carries:
+- =:grounding= — the specific Org heading from which it was extracted
+- =:provenance= — who or what produced it (gate-outcome, human-authored, deduced, LLM-proposed)
+- =:timestamp= — when it was admitted to the symbolic index
+- =:referenced-by= — other facts that depend on or reference this one
+- =:contradicted-by= — other facts that disagree with this one (if any)
+- =:superseded-by= — if this fact was replaced by a newer version
+
+These fields make every fact auditable. The =/audit <node-id>= command renders the full provenance chain as an Org headline tree. The provenance is not a logging feature. It is the product.